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种子名称： [FreeCourseSite.com] Udemy - Machine Learning with Imbalanced Data
文件类型： 视频
文件数目： 91个文件
文件大小： 2.94 GB
收录时间： 2021-8-10 11:41
已经下载： 3次
资源热度： 110
最近下载： 2024-9-4 08:23

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[FreeCourseSite.com] Udemy - Machine Learning with Imbalanced Data.torrent

1. Introduction/1. Introduction.mp432.25MB

1. Introduction/2. Course Curriculum Overview.mp417.54MB

1. Introduction/3. Course Material.mp410.96MB

2. Machine Learning with Imbalanced Data Overview/1. Imbalanced classes - Introduction.mp433.3MB

2. Machine Learning with Imbalanced Data Overview/2. Nature of the imbalanced class.mp435.11MB

2. Machine Learning with Imbalanced Data Overview/3. Approaches to work with imbalanced datasets - Overview.mp420.24MB

3. Evaluation Metrics/1. Introduction to Performance Metrics.mp410.79MB

3. Evaluation Metrics/10. Geometric Mean, Dominance, Index of Imbalanced Accuracy - Demo.mp486.77MB

3. Evaluation Metrics/11. ROC-AUC.mp439.25MB

3. Evaluation Metrics/12. ROC-AUC - Demo.mp431.56MB

3. Evaluation Metrics/13. Precision-Recall Curve.mp440.5MB

3. Evaluation Metrics/14. Precision-Recall Curve - Demo.mp418.08MB

3. Evaluation Metrics/16. Probability.mp420.64MB

3. Evaluation Metrics/2. Accuracy.mp421.44MB

3. Evaluation Metrics/3. Accuracy - Demo.mp447.61MB

3. Evaluation Metrics/4. Precision, Recall and F-measure.mp466.98MB

3. Evaluation Metrics/6. Precision, Recall and F-measure - Demo.mp480.33MB

3. Evaluation Metrics/7. Confusion tables, FPR and FNR.mp429.72MB

3. Evaluation Metrics/8. Confusion tables, FPR and FNR - Demo.mp449.08MB

3. Evaluation Metrics/9. Geometric Mean, Dominance, Index of Imbalanced Accuracy.mp423.06MB

4. Udersampling/1. Under-Sampling Methods - Introduction.mp431.45MB

4. Udersampling/10. Edited Nearest Neighbours - Intro.mp422.57MB

4. Udersampling/11. Edited Nearest Neighbours - Demo.mp430.82MB

4. Udersampling/12. Repeated Edited Nearest Neighbours - Intro.mp424.27MB

4. Udersampling/13. Repeated Edited Nearest Neighbours - Demo.mp422.89MB

4. Udersampling/14. All KNN - Intro.mp416.27MB

4. Udersampling/15. All KNN - Demo.mp422.65MB

4. Udersampling/16. Neighbourhood Cleaning Rule - Intro.mp423.04MB

4. Udersampling/17. Neighbourhood Cleaning Rule - Demo.mp415.9MB

4. Udersampling/18. NearMiss - Intro.mp417.18MB

4. Udersampling/19. NearMiss - Demo.mp426.33MB

4. Udersampling/2. Random Under-Sampling - Intro.mp425.62MB

4. Udersampling/20. Instance Hardness Threshold - Intro.mp419.7MB

4. Udersampling/21. Instance Hardness Threshold - Demo.mp430.54MB

4. Udersampling/22. Undersampling Method Comparison.mp447.52MB

4. Udersampling/3. Random Under-Sampling - Demo.mp466.91MB

4. Udersampling/4. Condensed Nearest Neighbours - Intro.mp432.43MB

4. Udersampling/5. Condensed Nearest Neighbours - Demo.mp452.71MB

4. Udersampling/6. Tomek Links - Intro.mp418.97MB

4. Udersampling/7. Tomek Links - Demo.mp423.98MB

4. Udersampling/8. One Sided Selection - Intro.mp411.9MB

4. Udersampling/9. One Sided Selection - Demo.mp425.59MB

5. Oversampling/1. Over-Sampling Methods - Introduction.mp421.09MB

5. Oversampling/10. Borderline SMOTE.mp446.2MB

5. Oversampling/11. Borderline SMOTE - Demo.mp424.77MB

5. Oversampling/12. SVM SMOTE.mp425.27MB

5. Oversampling/13. SVM SMOTE - Demo.mp437.01MB

5. Oversampling/14. K-Means SMOTE.mp427.6MB

5. Oversampling/15. K-Means SMOTE - Demo.mp424.77MB

5. Oversampling/16. Over-Sampling Method Comparison.mp439.77MB

5. Oversampling/2. Random Over-Sampling.mp415.65MB

5. Oversampling/3. Random Over-Sampling - Demo.mp435.2MB

5. Oversampling/4. SMOTE.mp444.61MB

5. Oversampling/5. SMOTE - Demo.mp418.38MB

5. Oversampling/6. SMOTE-NC.mp448.03MB

5. Oversampling/7. SMOTE-NC - Demo.mp421.43MB

5. Oversampling/8. ADASYN.mp431.6MB

5. Oversampling/9. ADASYN - Demo.mp420.95MB

6. Over and Undersampling/1. Combining Over and Under-sampling - Intro.mp436.9MB

6. Over and Undersampling/2. Combining Over and Under-sampling - Demo.mp434.33MB

6. Over and Undersampling/3. Comparison of Over and Under-sampling Methods.mp436.54MB

7. Ensemble Methods/1. Ensemble methods with Imbalanced Data.mp426.54MB

7. Ensemble Methods/2. Foundations of Ensemble Learning.mp419.71MB

7. Ensemble Methods/3. Bagging.mp418.19MB

7. Ensemble Methods/4. Bagging plus Over- or Under-Sampling.mp442.87MB

7. Ensemble Methods/5. Boosting.mp470.58MB

7. Ensemble Methods/6. Boosting plus Re-Sampling.mp447.31MB

7. Ensemble Methods/7. Hybdrid Methods.mp430.49MB

7. Ensemble Methods/8. Ensemble Methods - Demo.mp470.85MB

8. Cost Sensitive Learning/1. Cost-sensitive Learning - Intro.mp432.73MB

8. Cost Sensitive Learning/10. MetaCost.mp442.57MB

8. Cost Sensitive Learning/11. MetaCost - Demo.mp422.94MB

8. Cost Sensitive Learning/12. Optional MetaCost Base Code.mp436.92MB

8. Cost Sensitive Learning/2. Types of Cost.mp443.99MB

8. Cost Sensitive Learning/3. Obtaining the Cost.mp418.96MB

8. Cost Sensitive Learning/4. Cost Sensitive Approaches.mp410.33MB

8. Cost Sensitive Learning/5. Misclassification Cost in Logistic Regression.mp418.69MB

8. Cost Sensitive Learning/6. Misclassification Cost in Decision Trees.mp421.26MB

8. Cost Sensitive Learning/7. Cost Sensitive Learning with Scikit-learn- Demo.mp456.06MB

8. Cost Sensitive Learning/8. Find Optimal Cost with hyperparameter tuning.mp422.9MB

8. Cost Sensitive Learning/9. Bayes Conditional Risk.mp472.04MB

9. Probability Calibration/1. Probability Calibration.mp434.09MB

9. Probability Calibration/10. Calibrating a Classifier with Cost-sensitive Learning.mp425.19MB

9. Probability Calibration/2. Probability Calibration Curves.mp428.76MB

9. Probability Calibration/3. Probability Calibration Curves - Demo.mp464.88MB

9. Probability Calibration/4. Brier Score.mp417.15MB

9. Probability Calibration/5. Brier Score - Demo.mp449.02MB

9. Probability Calibration/6. Under- and Over-sampling and Cost-sensitive learning on Probability Calibration.mp429.58MB

9. Probability Calibration/7. Calibrating a Classifier.mp427.19MB

9. Probability Calibration/8. Calibrating a Classifier - Demo.mp446.73MB

9. Probability Calibration/9. Calibrating a Classfiier after SMOTE or Under-sampling.mp452MB